Less often but possible is someone with no real world experience studies to the test, hits these it out of the park, and then fails to have much impact in their job because they don’t learn to work with complex technical frameworks and organizational structures.
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They are also unfair to folks without an undergraduate computer science degree and arguably a masters/professional degree in statistics, data science, or CS-ML. That demographic is still largely high SES white/Asian men in the US.https://www.exploringcs.org/archives/cs-statistics …
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We don’t ask candidates to show the key skills to succeed in junior roles: navigate large, highly abstract codebases, makes sense of complex data systems, and complicated organizational structures—to ask the right questions and identify the right people to talk to.
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In fact, we often don’t even ask candidates to work with data. Sometimes we’ll put a highly structured example where we ask candidates to stream over a data set using Python or write hypothetical code to create a reasonable metric.
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I’ve had some success with take home interviews involving making sense of data. You get a good sense of a candidates strengths and weaknesses in exploratory analysis, a sense of their strategic thinking, and a sense of how well they form questions.
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Of course that means a bigger chunk of time that not every candidate has, esp if they have kids, particularly so during this pandemic. But I reject the notion that take home interviews are somehow harder on candidates than your typical suite of whiteboard technical interviews.
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Take homes definitely aren’t a cure all though. What’s worked well for you?
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Replying to @SolomonMg
As much as possible, interviews should replicate the actual job the candidate will be doing. Problem solving interviews (particularly when framed as a pairing exercise) seem to work well
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Replying to @imightbemary @SolomonMg
For example, once when I was hiring an MLE, I had them design an Airflow ETL to support their own modeling workflows. It was a great filter not only for skill set, but also for a mature mindset about what the job actually entailed
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As an interviewee, I once did a session where I was given a huge code block that I had to explain and then refactor. I’d definitely like to steal that idea for a panel at some point (probably soon)
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